Blog Archives

New Australian data on the HMD

November 26, 2014
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The Human Mortality Database is a wonderful resource for anyone interested in demographic data. It is a carefully curated collection of high quality deaths and population data from 37 countries, all in a consistent format with consistent definitions. I have used it many times and never cease to be amazed at the care taken to maintain

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Visualization of probabilistic forecasts

November 21, 2014
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Visualization of probabilistic forecasts

This week my research group discussed Adrian Raftery’s recent paper on “Use and Communication of Probabilistic Forecasts” which provides a fascinating but brief survey of some of his work on modelling and communicating uncertain futures. Coincidentally, today I was also sent a copy of David Spiegelhalter’s paper on “Visualizing Uncertainty About the Future”. Both are

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Seasonal periods

November 6, 2014
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I get questions about this almost every week. Here is an example from a recent comment on this blog: I have two large time series data. One is separated by seconds intervals and the other by minutes. The length of each time series is 180 days. I’m using R (3.1.1) for forecasting the data. I’d

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Jobs at Amazon

October 28, 2014
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I do not normally post job adverts, but this was very specifically targeted to “applied time series candidates” so I thought it might be of sufficient interest to readers of this blog. Here is an excerpt from an email I received from someone at Amazon: Amazon is aggressively recruiting in the data sciences, and we

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Prediction intervals too narrow

October 21, 2014
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Prediction intervals too narrow

Almost all prediction intervals from time series models are too narrow. This is a well-known phenomenon and arises because they do not account for all sources of uncertainty. In my 2002 IJF paper, we measured the size of the problem by computing the actual coverage percentage of the prediction intervals on hold-out samples. We found

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hts with regressors

October 19, 2014
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hts with regressors

The hts package for R allows for forecasting hierarchical and grouped time series data. The idea is to generate forecasts for all series at all levels of aggregation without imposing the aggregation constraints, and then to reconcile the forecasts so they satisfy the aggregation constraints. (An introduction to reconciling hierarchical and grouped time series is

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TBATS with regressors

October 5, 2014
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I’ve received a few emails about including regression variables (i.e., covariates) in TBATS models. As TBATS models are related to ETS models, tbats() is unlikely to ever include covariates as explained here. It won’t actually complain if you include an xreg argument, but it will ignore it. When I want to include covariates in a

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FPP now available as a downloadable e-book

September 20, 2014
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FPP now available as a downloadable e-book

My forecasting textbook with George Athanasopoulos is already available online (for free), and in print via Amazon (for under $40). Now we have made it available as a downloadable e-book via Google Books (for $15.55). The Google Books version is identical to the print version on Amazon (apart from a few typos that have been fixed). To use

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Generating quantile forecasts in R

September 7, 2014
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Generating quantile forecasts in R

From today’s email: I have just finished reading a copy of ‘Forecasting:Principles and Practice’ and I have found the book really interesting. I have particularly enjoyed the case studies and focus on practical applications. After finishing the book I have joined a forecasting competition to put what I’ve learnt to the test. I do have

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Resources for the FPP book

September 2, 2014
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The FPP resources page has recently been updated with several new additions including R code for all examples in the book. This was already available within each chapter, but the examples have been collected into one file per chapter to save copying and pasting the various code fragments. Slides from a course on Predictive Analytics from the University of Sydney....

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